Abstract
Linguistic typology studies the range of structures present in human language. The main goal of the field is to discover which sets of possible phenomena are universal, and which are merely frequent. For example, all languages have vowels, while most—but not all—languages have an /u/ sound. In this paper we present the first probabilistic treatment of a basic question in phonological typology: What makes a natural vowel inventory? We introduce a series of deep stochastic point processes, and contrast them with previous computational, simulation-based approaches. We provide a comprehensive suite of experiments on over 200 distinct languages.- Anthology ID:
- P17-1109
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1182–1192
- Language:
- URL:
- https://aclanthology.org/P17-1109
- DOI:
- 10.18653/v1/P17-1109
- Cite (ACL):
- Ryan Cotterell and Jason Eisner. 2017. Probabilistic Typology: Deep Generative Models of Vowel Inventories. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1182–1192, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Probabilistic Typology: Deep Generative Models of Vowel Inventories (Cotterell & Eisner, ACL 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/P17-1109.pdf